Climate change and the extreme weather have a negative impact on road traffic safety, resulting in severe road traffic accidents. In this study, a negative binomial model and a log-change model are proposed to analyse the impact of various factors on fatal traffic accidents. The dataset used in this study includes the fatal traffic accident frequency, social development indicators and climate indicators in California and Arizona. The results show that both models can provide accurate fitting results. Climate variables (i.e., average temperature and standard precipitation 24) can significantly affect the frequency of fatal traffic accidents. Non-climate variables (i.e., beer consumption, rural Vehicle miles travelled ratio, and vehicle performance) also have a significant impact. The modelling results can provide decision-making guidelines for the transportation management agencies to improve road traffic safety.

In complex lane change (LC) scenarios, semantic interpretation and safety analysis of dynamic interaction pattern are necessary for autonomous vehicles to make appropriate decisions. This study proposes a learning framework that combines primitive-based interaction pattern recognition and risk analysis. The Hidden Markov Model with the Gaussian mixture model (GMM-HMM) approach is developed to decompose the LC scenarios into primitives. Then K-means clustering with Dynamic Time Warping (DTW) is applied to gather the primitives into 13 LC interaction patterns. Finally, this study considers time-to-collision (TTC) of two conflict types involved in the LC process. And the TTC is used to analyze the risk of interaction patterns and extract high-risk LC interaction patterns. The LC events obtained from the Highway Drone Dataset (highD) demonstrate that the identified LC interaction patterns contain interpretable semantic information. This study identifies the dynamic spatiotemporal characteristics and risk formation mechanism of the LC interaction patterns. The findings are useful to comprehensively understand the latent interaction patterns, which can then be used to design and improve the decision-making process during lane changes and enhance the safety of autonomous vehicle.

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